Overview

Dataset statistics

Number of variables13
Number of observations14620
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory104.0 B

Variable types

Numeric12
Categorical1

Alerts

number of bedrooms is highly overall correlated with number of bathrooms and 2 other fieldsHigh correlation
number of bathrooms is highly overall correlated with number of bedrooms and 6 other fieldsHigh correlation
living area is highly overall correlated with number of bedrooms and 4 other fieldsHigh correlation
number of floors is highly overall correlated with number of bathrooms and 3 other fieldsHigh correlation
grade of the house is highly overall correlated with number of bathrooms and 4 other fieldsHigh correlation
Area of the house(excluding basement) is highly overall correlated with number of bedrooms and 5 other fieldsHigh correlation
Built Year is highly overall correlated with number of bathrooms and 1 other fieldsHigh correlation
Price is highly overall correlated with number of bathrooms and 3 other fieldsHigh correlation
Area of the basement has 8842 (60.5%) zerosZeros

Reproduction

Analysis started2023-05-17 07:39:49.335920
Analysis finished2023-05-17 07:40:11.977080
Duration22.64 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

number of bedrooms
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3793434
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2023-05-17T13:10:12.048564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range32
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.93871885
Coefficient of variation (CV)0.27778144
Kurtosis69.24031
Mean3.3793434
Median Absolute Deviation (MAD)1
Skewness2.6632569
Sum49406
Variance0.88119308
MonotonicityNot monotonic
2023-05-17T13:10:12.141431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 6612
45.2%
4 4724
32.3%
2 1844
 
12.6%
5 1079
 
7.4%
6 176
 
1.2%
1 136
 
0.9%
7 30
 
0.2%
8 11
 
0.1%
9 3
 
< 0.1%
10 3
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
1 136
 
0.9%
2 1844
 
12.6%
3 6612
45.2%
4 4724
32.3%
5 1079
 
7.4%
6 176
 
1.2%
7 30
 
0.2%
8 11
 
0.1%
9 3
 
< 0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
33 1
 
< 0.1%
11 1
 
< 0.1%
10 3
 
< 0.1%
9 3
 
< 0.1%
8 11
 
0.1%
7 30
 
0.2%
6 176
 
1.2%
5 1079
 
7.4%
4 4724
32.3%
3 6612
45.2%

number of bathrooms
Real number (ℝ)

Distinct29
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1295828
Minimum0.5
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2023-05-17T13:10:12.277791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range7.5
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.7699345
Coefficient of variation (CV)0.36154242
Kurtosis1.5881949
Mean2.1295828
Median Absolute Deviation (MAD)0.5
Skewness0.55666314
Sum31134.5
Variance0.59279913
MonotonicityNot monotonic
2023-05-17T13:10:12.448242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2.5 3678
25.2%
1 2509
17.2%
1.75 2062
14.1%
2.25 1378
 
9.4%
2 1323
 
9.0%
1.5 968
 
6.6%
2.75 831
 
5.7%
3 510
 
3.5%
3.5 504
 
3.4%
3.25 424
 
2.9%
Other values (19) 433
 
3.0%
ValueCountFrequency (%)
0.5 3
 
< 0.1%
0.75 47
 
0.3%
1 2509
17.2%
1.25 7
 
< 0.1%
1.5 968
 
6.6%
1.75 2062
14.1%
2 1323
 
9.0%
2.25 1378
 
9.4%
2.5 3678
25.2%
2.75 831
 
5.7%
ValueCountFrequency (%)
8 2
 
< 0.1%
7.75 1
 
< 0.1%
7.5 1
 
< 0.1%
6.75 2
 
< 0.1%
6.5 1
 
< 0.1%
6.25 2
 
< 0.1%
6 3
 
< 0.1%
5.75 2
 
< 0.1%
5.5 8
0.1%
5.25 12
0.1%

living area
Real number (ℝ)

Distinct865
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2098.263
Minimum370
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2023-05-17T13:10:12.614989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile940
Q11440
median1930
Q32570
95-th percentile3800
Maximum13540
Range13170
Interquartile range (IQR)1130

Descriptive statistics

Standard deviation928.27572
Coefficient of variation (CV)0.44240199
Kurtosis6.0736171
Mean2098.263
Median Absolute Deviation (MAD)550
Skewness1.5383366
Sum30676605
Variance861695.81
MonotonicityNot monotonic
2023-05-17T13:10:12.789553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 93
 
0.6%
1010 92
 
0.6%
1320 91
 
0.6%
1660 90
 
0.6%
1820 88
 
0.6%
1440 86
 
0.6%
2100 85
 
0.6%
1800 84
 
0.6%
1720 84
 
0.6%
1480 84
 
0.6%
Other values (855) 13743
94.0%
ValueCountFrequency (%)
370 1
< 0.1%
380 1
< 0.1%
420 1
< 0.1%
430 1
< 0.1%
440 1
< 0.1%
460 1
< 0.1%
470 1
< 0.1%
480 2
< 0.1%
490 1
< 0.1%
500 1
< 0.1%
ValueCountFrequency (%)
13540 1
< 0.1%
12050 1
< 0.1%
10040 1
< 0.1%
9890 1
< 0.1%
9640 1
< 0.1%
9200 1
< 0.1%
8670 1
< 0.1%
8020 1
< 0.1%
8010 1
< 0.1%
7710 1
< 0.1%

lot area
Real number (ℝ)

Distinct7451
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15093.281
Minimum520
Maximum1074218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2023-05-17T13:10:13.045408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1752.9
Q15010.75
median7620
Q310800
95-th percentile43832.3
Maximum1074218
Range1073698
Interquartile range (IQR)5789.25

Descriptive statistics

Standard deviation37919.621
Coefficient of variation (CV)2.5123511
Kurtosis164.75727
Mean15093.281
Median Absolute Deviation (MAD)2655
Skewness10.155206
Sum2.2066377 × 108
Variance1.4378977 × 109
MonotonicityNot monotonic
2023-05-17T13:10:13.247137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 269
 
1.8%
6000 176
 
1.2%
4000 172
 
1.2%
7200 149
 
1.0%
7500 82
 
0.6%
4800 80
 
0.5%
4500 75
 
0.5%
9600 73
 
0.5%
9000 72
 
0.5%
8400 72
 
0.5%
Other values (7441) 13400
91.7%
ValueCountFrequency (%)
520 1
< 0.1%
635 1
< 0.1%
638 1
< 0.1%
676 1
< 0.1%
681 1
< 0.1%
696 1
< 0.1%
704 1
< 0.1%
705 1
< 0.1%
711 1
< 0.1%
713 1
< 0.1%
ValueCountFrequency (%)
1074218 1
< 0.1%
982998 1
< 0.1%
982278 1
< 0.1%
843309 1
< 0.1%
641203 1
< 0.1%
577605 1
< 0.1%
533610 1
< 0.1%
507038 1
< 0.1%
505166 1
< 0.1%
501376 1
< 0.1%

number of floors
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5023598
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2023-05-17T13:10:13.341075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.54023861
Coefficient of variation (CV)0.35959337
Kurtosis-0.52357613
Mean1.5023598
Median Absolute Deviation (MAD)0.5
Skewness0.58615758
Sum21964.5
Variance0.29185776
MonotonicityNot monotonic
2023-05-17T13:10:13.483791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 7103
48.6%
2 5666
38.8%
1.5 1311
 
9.0%
3 418
 
2.9%
2.5 118
 
0.8%
3.5 4
 
< 0.1%
ValueCountFrequency (%)
1 7103
48.6%
1.5 1311
 
9.0%
2 5666
38.8%
2.5 118
 
0.8%
3 418
 
2.9%
3.5 4
 
< 0.1%
ValueCountFrequency (%)
3.5 4
 
< 0.1%
3 418
 
2.9%
2.5 118
 
0.8%
2 5666
38.8%
1.5 1311
 
9.0%
1 7103
48.6%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size828.2 KiB
3
9350 
4
3874 
5
1278 
2
 
100
1
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14620
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row3
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3 9350
64.0%
4 3874
26.5%
5 1278
 
8.7%
2 100
 
0.7%
1 18
 
0.1%

Length

2023-05-17T13:10:13.677093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-17T13:10:13.918694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3 9350
64.0%
4 3874
26.5%
5 1278
 
8.7%
2 100
 
0.7%
1 18
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 9350
64.0%
4 3874
26.5%
5 1278
 
8.7%
2 100
 
0.7%
1 18
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14620
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 9350
64.0%
4 3874
26.5%
5 1278
 
8.7%
2 100
 
0.7%
1 18
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 9350
64.0%
4 3874
26.5%
5 1278
 
8.7%
2 100
 
0.7%
1 18
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 9350
64.0%
4 3874
26.5%
5 1278
 
8.7%
2 100
 
0.7%
1 18
 
0.1%

grade of the house
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6824213
Minimum4
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2023-05-17T13:10:14.035267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1750328
Coefficient of variation (CV)0.15295083
Kurtosis1.0480222
Mean7.6824213
Median Absolute Deviation (MAD)1
Skewness0.77758351
Sum112317
Variance1.380702
MonotonicityNot monotonic
2023-05-17T13:10:14.154413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 6011
41.1%
8 4137
28.3%
9 1828
 
12.5%
6 1324
 
9.1%
10 804
 
5.5%
11 280
 
1.9%
5 154
 
1.1%
12 55
 
0.4%
4 17
 
0.1%
13 10
 
0.1%
ValueCountFrequency (%)
4 17
 
0.1%
5 154
 
1.1%
6 1324
 
9.1%
7 6011
41.1%
8 4137
28.3%
9 1828
 
12.5%
10 804
 
5.5%
11 280
 
1.9%
12 55
 
0.4%
13 10
 
0.1%
ValueCountFrequency (%)
13 10
 
0.1%
12 55
 
0.4%
11 280
 
1.9%
10 804
 
5.5%
9 1828
 
12.5%
8 4137
28.3%
7 6011
41.1%
6 1324
 
9.1%
5 154
 
1.1%
4 17
 
0.1%
Distinct781
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1801.7839
Minimum370
Maximum9410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2023-05-17T13:10:14.336589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile860
Q11200
median1580
Q32240
95-th percentile3400
Maximum9410
Range9040
Interquartile range (IQR)1040

Descriptive statistics

Standard deviation833.80996
Coefficient of variation (CV)0.46276912
Kurtosis3.4022583
Mean1801.7839
Median Absolute Deviation (MAD)460
Skewness1.4364458
Sum26342081
Variance695239.05
MonotonicityNot monotonic
2023-05-17T13:10:14.472270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1010 146
 
1.0%
1200 138
 
0.9%
1300 138
 
0.9%
1140 127
 
0.9%
1400 126
 
0.9%
1220 125
 
0.9%
1340 123
 
0.8%
1060 123
 
0.8%
1320 118
 
0.8%
1100 117
 
0.8%
Other values (771) 13339
91.2%
ValueCountFrequency (%)
370 1
 
< 0.1%
380 1
 
< 0.1%
420 1
 
< 0.1%
430 1
 
< 0.1%
440 1
 
< 0.1%
460 1
 
< 0.1%
470 1
 
< 0.1%
480 3
< 0.1%
490 2
< 0.1%
500 2
< 0.1%
ValueCountFrequency (%)
9410 1
< 0.1%
8860 1
< 0.1%
8570 1
< 0.1%
8020 1
< 0.1%
7680 1
< 0.1%
7320 1
< 0.1%
6640 1
< 0.1%
6430 1
< 0.1%
6420 1
< 0.1%
6380 1
< 0.1%

Area of the basement
Real number (ℝ)

Distinct280
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean296.47907
Minimum0
Maximum4820
Zeros8842
Zeros (%)60.5%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2023-05-17T13:10:14.618281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3580
95-th percentile1190
Maximum4820
Range4820
Interquartile range (IQR)580

Descriptive statistics

Standard deviation448.55141
Coefficient of variation (CV)1.5129277
Kurtosis3.1396354
Mean296.47907
Median Absolute Deviation (MAD)0
Skewness1.6097443
Sum4334524
Variance201198.37
MonotonicityNot monotonic
2023-05-17T13:10:14.856089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8842
60.5%
800 157
 
1.1%
600 148
 
1.0%
700 147
 
1.0%
500 146
 
1.0%
400 121
 
0.8%
1000 107
 
0.7%
900 98
 
0.7%
300 96
 
0.7%
620 71
 
0.5%
Other values (270) 4687
32.1%
ValueCountFrequency (%)
0 8842
60.5%
10 2
 
< 0.1%
20 1
 
< 0.1%
40 1
 
< 0.1%
50 10
 
0.1%
60 7
 
< 0.1%
65 1
 
< 0.1%
70 4
 
< 0.1%
80 10
 
0.1%
90 14
 
0.1%
ValueCountFrequency (%)
4820 1
< 0.1%
4130 1
< 0.1%
3500 1
< 0.1%
3480 1
< 0.1%
3260 1
< 0.1%
3000 1
< 0.1%
2850 1
< 0.1%
2810 1
< 0.1%
2730 1
< 0.1%
2720 1
< 0.1%

Built Year
Real number (ℝ)

Distinct116
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1970.9264
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2023-05-17T13:10:15.023711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11951
median1975
Q31997
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.493625
Coefficient of variation (CV)0.014964346
Kurtosis-0.67347362
Mean1970.9264
Median Absolute Deviation (MAD)23
Skewness-0.47204858
Sum28814944
Variance869.87392
MonotonicityNot monotonic
2023-05-17T13:10:15.174231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 404
 
2.8%
2005 319
 
2.2%
2006 300
 
2.1%
2004 296
 
2.0%
2003 295
 
2.0%
2007 274
 
1.9%
1977 266
 
1.8%
1978 264
 
1.8%
1968 253
 
1.7%
2008 241
 
1.6%
Other values (106) 11708
80.1%
ValueCountFrequency (%)
1900 61
0.4%
1901 21
 
0.1%
1902 20
 
0.1%
1903 33
0.2%
1904 28
 
0.2%
1905 46
0.3%
1906 67
0.5%
1907 49
0.3%
1908 54
0.4%
1909 72
0.5%
ValueCountFrequency (%)
2015 12
 
0.1%
2014 404
2.8%
2013 130
 
0.9%
2012 103
 
0.7%
2011 98
 
0.7%
2010 85
 
0.6%
2009 148
 
1.0%
2008 241
1.6%
2007 274
1.9%
2006 300
2.1%

Postal Code
Real number (ℝ)

Distinct70
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122033.06
Minimum122003
Maximum122072
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2023-05-17T13:10:15.373071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum122003
5-th percentile122006
Q1122017
median122032
Q3122048
95-th percentile122066
Maximum122072
Range69
Interquartile range (IQR)31

Descriptive statistics

Standard deviation19.082418
Coefficient of variation (CV)0.00015637089
Kurtosis-1.0583636
Mean122033.06
Median Absolute Deviation (MAD)16
Skewness0.22773543
Sum1.7841234 × 109
Variance364.13868
MonotonicityNot monotonic
2023-05-17T13:10:15.576782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122028 432
 
3.0%
122005 416
 
2.8%
122006 397
 
2.7%
122007 396
 
2.7%
122033 383
 
2.6%
122024 376
 
2.6%
122027 347
 
2.4%
122035 343
 
2.3%
122018 335
 
2.3%
122038 322
 
2.2%
Other values (60) 10873
74.4%
ValueCountFrequency (%)
122003 130
 
0.9%
122004 157
 
1.1%
122005 416
2.8%
122006 397
2.7%
122007 396
2.7%
122008 198
1.4%
122009 216
1.5%
122010 291
2.0%
122011 229
1.6%
122012 285
1.9%
ValueCountFrequency (%)
122072 132
0.9%
122071 37
 
0.3%
122070 83
 
0.6%
122069 89
 
0.6%
122068 154
1.1%
122067 179
1.2%
122066 87
 
0.6%
122065 155
1.1%
122064 253
1.7%
122063 185
1.3%

Distance from the airport
Real number (ℝ)

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.950958
Minimum50
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2023-05-17T13:10:15.762797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile51
Q157
median65
Q373
95-th percentile79
Maximum80
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.9360078
Coefficient of variation (CV)0.13758085
Kurtosis-1.2030482
Mean64.950958
Median Absolute Deviation (MAD)8
Skewness0.0061143303
Sum949583
Variance79.852236
MonotonicityNot monotonic
2023-05-17T13:10:15.942764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
54 514
 
3.5%
70 511
 
3.5%
64 500
 
3.4%
50 495
 
3.4%
79 487
 
3.3%
60 486
 
3.3%
67 485
 
3.3%
56 484
 
3.3%
53 484
 
3.3%
59 482
 
3.3%
Other values (21) 9692
66.3%
ValueCountFrequency (%)
50 495
3.4%
51 442
3.0%
52 447
3.1%
53 484
3.3%
54 514
3.5%
55 473
3.2%
56 484
3.3%
57 467
3.2%
58 474
3.2%
59 482
3.3%
ValueCountFrequency (%)
80 454
3.1%
79 487
3.3%
78 461
3.2%
77 465
3.2%
76 458
3.1%
75 466
3.2%
74 471
3.2%
73 466
3.2%
72 471
3.2%
71 465
3.2%

Price
Real number (ℝ)

Distinct2901
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean538932.22
Minimum78000
Maximum7700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.3 KiB
2023-05-17T13:10:16.144954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum78000
5-th percentile210000
Q1320000
median450000
Q3645000
95-th percentile1150000
Maximum7700000
Range7622000
Interquartile range (IQR)325000

Descriptive statistics

Standard deviation367532.38
Coefficient of variation (CV)0.68196402
Kurtosis40.321918
Mean538932.22
Median Absolute Deviation (MAD)150000
Skewness4.2692977
Sum7.879189 × 109
Variance1.3508005 × 1011
MonotonicityNot monotonic
2023-05-17T13:10:16.324479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450000 114
 
0.8%
350000 113
 
0.8%
400000 104
 
0.7%
375000 103
 
0.7%
550000 102
 
0.7%
500000 101
 
0.7%
300000 97
 
0.7%
425000 93
 
0.6%
250000 92
 
0.6%
525000 88
 
0.6%
Other values (2891) 13613
93.1%
ValueCountFrequency (%)
78000 1
 
< 0.1%
80000 1
 
< 0.1%
82000 1
 
< 0.1%
82500 1
 
< 0.1%
83000 1
 
< 0.1%
85000 1
 
< 0.1%
86500 1
 
< 0.1%
89000 1
 
< 0.1%
90000 3
< 0.1%
92000 1
 
< 0.1%
ValueCountFrequency (%)
7700000 1
< 0.1%
7060000 1
< 0.1%
6890000 1
< 0.1%
5570000 1
< 0.1%
5110000 1
< 0.1%
4670000 1
< 0.1%
4490000 1
< 0.1%
4000000 1
< 0.1%
3850000 1
< 0.1%
3800000 2
< 0.1%

Interactions

2023-05-17T13:10:09.626571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:50.264918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:52.042592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:53.784180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:55.386305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:57.117917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:58.694647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:00.503537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:02.506483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:04.204888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:05.973353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:07.729707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:09.776185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:50.412939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-17T13:10:02.714650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-17T13:09:52.369782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:54.067527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:55.658685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:57.346637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:59.046585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:00.819842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:02.874761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:04.413770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:06.258629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:08.083832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:10.046894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:50.706033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-17T13:10:04.518793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:06.393529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-17T13:10:03.588470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:05.274946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:07.146625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:09.029362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:11.030627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:51.542385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:53.370255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:54.981114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:56.728713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:58.285345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:00.042235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:01.935061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:03.757427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:05.466400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:07.275477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:09.185954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:11.175698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:51.711518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:53.514001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:55.103700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:56.848918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:58.391003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:00.267332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:02.065522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:03.928496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:05.645673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:07.403338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:09.332278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:11.295269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:51.885951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:53.649482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:55.252504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:56.983993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:09:58.535133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:00.364469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:02.298197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:04.085195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:05.817766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:07.580388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-17T13:10:09.485903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-17T13:10:16.587084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
number of bedroomsnumber of bathroomsliving arealot areanumber of floorsgrade of the houseArea of the house(excluding basement)Area of the basementBuilt YearPostal CodeDistance from the airportPricecondition of the house
number of bedrooms1.0000.5210.6470.2120.2290.3830.5390.2290.180-0.047-0.0070.3510.012
number of bathrooms0.5211.0000.7420.0660.5510.6570.6880.1920.561-0.1150.0080.5080.121
living area0.6470.7421.0000.3020.4020.7180.8430.3280.345-0.0980.0020.6580.053
lot area0.2120.0660.3021.000-0.2200.1500.2740.029-0.0340.109-0.0070.0800.017
number of floors0.2290.5510.402-0.2201.0000.5090.602-0.2710.547-0.1270.0180.3310.183
grade of the house0.3830.6570.7180.1500.5091.0000.7150.0880.495-0.1600.0050.6720.131
Area of the house(excluding basement)0.5390.6880.8430.2740.6020.7151.000-0.1670.468-0.0920.0070.5540.108
Area of the basement0.2290.1920.3280.029-0.2710.088-0.1671.000-0.184-0.0230.0010.2540.100
Built Year0.1800.5610.345-0.0340.5470.4950.468-0.1841.000-0.071-0.0020.1040.256
Postal Code-0.047-0.115-0.0980.109-0.127-0.160-0.092-0.023-0.0711.0000.012-0.2930.062
Distance from the airport-0.0070.0080.002-0.0070.0180.0050.0070.001-0.0020.0121.0000.0050.004
Price0.3510.5080.6580.0800.3310.6720.5540.2540.104-0.2930.0051.0000.024
condition of the house0.0120.1210.0530.0170.1830.1310.1080.1000.2560.0620.0040.0241.000

Missing values

2023-05-17T13:10:11.484203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-17T13:10:11.794775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

number of bedroomsnumber of bathroomsliving arealot areanumber of floorscondition of the housegrade of the houseArea of the house(excluding basement)Area of the basementBuilt YearPostal CodeDistance from the airportPrice
052.50365090502.051033702801921122003582380000
142.50292040001.558191010101909122004511400000
252.75291094801.538291001939122004531200000
342.503310429982.03933100200112200576838000
432.00271045001.5481880830192912200651805000
532.50260047501.0491700900195112200767790000
653.253660119952.031036600200612200872785000
731.752240105782.0581550690192312200671750000
832.50239065501.0481440950195512200973750000
942.252200112501.5571300900192012201053698000
number of bedroomsnumber of bathroomsliving arealot areanumber of floorscondition of the housegrade of the houseArea of the house(excluding basement)Area of the basementBuilt YearPostal CodeDistance from the airportPrice
1461042.75181073501.0471200610198012206573272000
1461131.75135076861.03713500198712202470261000
1461231.00118053501.54611800195912206354260000
1461331.001400104251.04714000196812204059241500
1461431.75159079311.0371190400197912202480240000
1461521.501556200001.04715560195712206676221700
1461632.00168070001.54716800196812207259219200
1461721.00107061201.03610700196212205664209000
1461841.00103066211.04610300195512204254205000
1461931.0090047701.0369000196912201855146000